Myocardial infarction (MI), a main cause of heart failure, leads to irreversible cardiomyocytes loss and cardiac function impairment. Current clinical treatments for MI are largely ineffective as they mostly aim to alleviate symptoms rather than repairing the injured myocardium. Thus, development of more effective therapies is compelling. This study aims to investigate whether the extracellular vesicles (EVs) carrying specific anti-apoptotic miRNA can be efficiently internalized into myocardium to achieve desired therapeutic outcomes.

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.